Complex networks play a critical role in modern societies. Electric power grids, gas pipeline systems, telecommunications systems, and aviation networks are but a few examples. A failure in even a portion of such networks can result in massive economic losses and/or significant disruption to peoples' lives, as well as to industrial, commercial and/or government activities. Operation of these complex networks can be an extremely challenging task due to their complex structures, wide geographical coverage, and sophisticated data/information technology systems. Many of the networks also exhibit highly dynamic and non-linear behaviors, with numerous network configurations. Furthermore, they can be affected by a number of external factors, including, but not limited to, physical attack, cyber threat, human error, and natural disasters. Typically, very little of the complex network operation is fully automated and human-in-the-loop operation is essential. In many instances it would not be uncommon for human operators to examine thousands of possible configurations in near real time to choose the best option and operate the network effectively. Given the lack of automation, network operation has to be largely based on operator experience, with very limited real-time decision support. Inability to process the large amounts of data and to manage the complexity can result in an inability to recognize, anticipate, and respond when situations arise that may lead to network failures. Therefore, there is a need for methods and systems of processing large amounts of operation data in order to transform such data into actionable information.